Multivariate profiling on an individual's biological makeup for medical, prognostic and personal use is becoming commonplace. These omic techniques encompass various modalities such as genomic, proteomic, metabolomic, epigenomic, and metagenomic profiling. In particular, genetic sequencing and profiling technology has advanced rapidly in recent years. The cost of genome sequencing is plummeting, while the availability of genomic sequencing technology is becoming more prevalent around the world. Simultaneously, we are rapidly improving our ability to draw meaningful personal health information from genomic data. We are quickly moving towards an environment in which individuals will be able to affordably have their whole genome sequenced and utilized regularly for personalized health insight and medical treatment. This may also be accompanied by a rapid proliferation of omic transactions between two or more participating entities in scenarios such as two individuals wanting to compare their omic information to determine their compatibility in terms of health of future progeny.
However, personal genome sequencing gives rise to significant challenges relating to privacy, information authentication and information verification. Genetic sequence data can reveal highly sensitive information about an individual, including the presence or propensity to develop genetic diseases and conditions, and even behavioral predispositions. Malicious use of genetic data could lead to privacy violation, genetic discrimination, and other harmful results. Therefore, individuals may desire to maintain their genetic information private from other people against whom they would like to test for potential compatibility, such as propensity for genetic disease in potential offspring, as well as from doctors and service providers who may require access to a limited portion of genetic information for limited purposes. Accordingly, to unlock the full potential benefits of genetic sequencing and analysis, it may be important to provide mechanisms for preserving the privacy of genomic sequence data.
One particularly valuable use of genomic computation is for evaluating the compatibility of individuals for purposes of having children, and specifically for identifying potential risks of genetic disease or other attributes in the potential offspring. Individuals being tested for compatibility may desire to learn specific information regarding their potential offspring, while avoiding or minimizing any potential disclosure of their own genetic information. Solutions to this issue have been proposed. One approach is for individuals to each provide their genomic data to a trusted third party for analysis, with the primary parties receiving only the results of the testing. However, in such a scenario, a participant's genomic privacy could be readily violated as a result of malicious action on or by the third party testing facility, such as a hacking attack, employee misconduct or organizational misuse. Furthermore, with such testing facilities potentially acting as centralized repositories for highly sensitive genetic information, they may be particularly susceptible likely to be targeted for attack.
Another approach to preserve privacy in genomic transactions is to utilize combinations of data encryption and computational techniques in order to enable calculations on genomic data, without revealing the entirety of that genomic data to any one party. Such techniques are described in, e.g., PCT Patent Publication Nos. WO 2014/040964 A1 and WO 2013/067542 A1 and WO 2008/135951 A1. One such technique that has been applied to genomic data is Secure Multiparty Computation (hereinafter, “SMC”). SMC techniques, such as Yao's Garbled Circuits technique, enable two parties to jointly compute a function while keeping their inputs private. SMC has been utilized to enable two individuals to test their genetic compatibility without disclosing their gene sequence data to one another.
Another approach to computational privacy is homomorphic encryption. In theory, homomorphic encryption techniques enable individuals to perform computations on encrypted data, without decrypting the data, thereby yielding a computationally sound result of a calculation without disclosing the input data.
While computational privacy techniques such as SMC and homomorphic encryption may protect against malicious breach of genetic privacy, they are also highly computationally intensive. As such, for certain applications they may require a burdensome or even impractical amount of time or computational resources. Also, traditional SMC and homomorphic encryption approaches may not address other characteristics that may be desirable in a platform for genomic computation. For example, in a computation platform testing for genetic compatibility between potential mates, it may be important to provide for verification of data integrity to ensure that the other party's genomic data has not been intentionally altered or unintentionally corrupted. Users or operators of such a platform may also desire to provide for data authentication, to verify that provided genomic data actually belongs to the intended individual. The success and desirability of certain genomic computation platforms may also require a convenient mechanism by which users can securely interact with the platform. Some of these and other factors may be addressed by certain of the embodiments described hereinbelow.